Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

Alcherio Martinoli, Zeynab Talebpour
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Article

Résumé

In this letter, we propose an adaptive risk-based replanning strategy in the context of multirobot task allocation for dealing with limitations of local perception and unpredicted human behavior. Our replanning method is based on the variations of social risk and humanmotion prediction uncertainty. The performance of our method is studied through an extensive suite of experiments of increasing complexity. Results obtained using both a high-fidelity simulator and real robots confirm that this strategy outperforms a nonadaptive replanning strategy in all cases with respect to the chosen social metrics. First, the overall performance of the team depends on its replanning strategy, and second on the available information about the humans. Although an adaptive replanning strategy with global perception leads to the best performance, it is computationally expensive and infeasible in some real applications. Local perception shows comparable results as long as updates of relevant human poses affecting a task's risk are available within the execution time of that task. Conversely, the nonadaptive replanning strategy is shown to have degraded results with global perception as decisions in this case can be based on outdated information that lead to invalid plans.

Official source

https://infoscience.epfl.ch/record/269263?ln=fr

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés

Chargement

Publications associées

Chargement

Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

Alcherio Martinoli, Zeynab Talebpour

In this paper, we propose an adaptive risk-based replanning strategy in the context of multi-robot task allocation for dealing with limitations of local perception and unpredicted human behavior. Our replanning method is based on the variations of social risk and human motion prediction uncertainty. The performance of our method is studied through an extensive suite of experiments of increasing complexity. Results obtained using both a high-fidelity simulator and real robots confirm that this strategy outperforms a non-adaptive replanning strategy in all cases with respect to the chosen social metrics. The overall performance of the team depends firstly on its replanning strategy, and secondly on the available information about the humans. Although an adaptive replanning strategy with global perception leads to the best performance, it is computationally expensive and infeasible in some real applications. Local perception shows comparable results as long as updates of relevant human poses affecting a task's risk are available within the execution time of that task. Conversely, the non-adaptive replanning strategy is shown to have degraded results with global perception as decisions in this case can be based on outdated information that lead to invalid plans.

Chargement

Enactive robot vision

Mototaka Suzuki

The complexity of today's autonomous robots poses a major challenge for Artificial Intelligence. These robots are equipped with sophisticated sensors and mechanical abilities that allow them to enter our homes and interact with humans. For example, today's robots are almost all equipped with vision and several of them can move over rough terrain with wheels or legs. The methods developed so far in Artificial Intelligence, however, are not yet ready to cope with the complexity of the information gathered through the robot sensors and the need for rapid action in partially unknown and dynamic environments. In this thesis, I will argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same time scale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. This line of work is grounded on philosophical and psychological research showing that perception is an active process mediated by behavior. However, computational models of active vision are very rare and often rely on architectures that are preprogrammed to detect certain characteristics of the environment. Previous work have shown that complex visual tasks, such as position and size invariant shape recognition as well as navigation, can be tackled with remarkably simple neural architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons were evolved while freely interacting with their environments. I proceed further on this line of investigation and describe the application of this methodology in three situations, namely car driving with an omnidirectional camera, goal-oriented navigation of a humanoid robot, and cooperative tasks by two agents. I will show that these systems develop sensitivity to a number of oriented, retinotopic, visual features – oriented edges, corners, height – and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system that allow them to accomplish their goals. In a second set of experiments, I will show that active vision can be exploited by the robot to perform anticipatory exploration of the environment in a task that requires landmark-based navigation. Evolved robots exploit an internal expectation system that makes use of active exploration to check for expected events in the environment. I will describe a third set of experiments where, in addition to an evolutionary process, the visual system of the robot can develop receptive fields by means of unsupervised Hebbian learning and show that these receptive fields are significantly affected by the behavior of the system and differ from those predicted by most computational models of visual cortex. Finally, I will show that these robots replicate the performance deficiencies observed in experiments of motor deprivation with kitten when they are exposed to the same type of motor deprivations. Furthermore, the analyses of our robot brains suggest an explanation for the deficiencies observed in kitten that have not yet been fully understood.

EPFL2007

Chargement

Reaching over to grasp an item is arguably the most commonly used motor skill by humans. Even under sudden perturbations, humans seem to react rapidly and adapt their motion to guarantee success. Despite the apparent ease and frequency with which we use this ability, a complete understanding of the underlying mechanisms cannot be claimed. It is partly due to such incomplete knowledge that adaptive robot motion for reaching and grasping under perturbations is not perfectly achieved. In this thesis, we take the discriminative approach for modelling trajectories of reach-to-grasp motion from expert demonstrations. Throughout this thesis, we will employ time-independent (autonomous) flow based representations to learn reactive motion controllers which can then be ported onto robots. This thesis is divided into three main parts. The first part is dedicated to biologically inspired modelling of reach-to-grasp motions with respect to the hand-arm coupling. We build upon previous work in motion modelling using autonomous dynamical systems (DS) and present a coupled dynamical system (CDS) model of these two subsystems. The coupled model ensures satisfaction of the constraints between the hand and the arm subsystems which are critical to the success of a reach-to-grasp task. Moreover, it reduces the complexity of the overall motion planning problem as compared to considering a combined problem for the hand and the arm motion. In the second part we extend the CDS approach to incorporate multiple grasping points. Such a model is beneficial due to the fact that many daily life objects afford multiple grasping locations on their surface. We combine a DS based approach with energy-function learning to learn a multiple attractor dynamical system where the attractors are mapped to the desired grasping points. We present the Augmented-SVM (ASVM) model that combines the classical SVM formulation with gradient constraints arising from the energy function to learn the desired dynamical function for motion generation. In the last part of this thesis, we address the problem of inverse-kinematics and obstacle avoidance by combining our flow-based motion generator with global configuration-space planners. We claim that the two techniques complement each other. On one hand, the fast reactive nature of our flow based motion generator can used to guide the search of a randomly exploring random tree (RRT) based global planner. On the other hand, global planners can efficiently handle arbitrary obstacles and avoid local minima present in the dynamical function learned from demonstrations. We show that combining the information from demonstrations with global planning in the form of a energy-map considerably decreases the computational complexity of state-of-the-art sampling based planners. We believe that this thesis has the following contributions to Robotics and Machine Learning. First, we have developed algorithms for fast and adaptive motion generation for reach-grasp motions. Second, we formulated an extension to the classical SVM formulation that takes into account the gradient information from data. We showed that instead of being limited as a classifier or a regressor, the SVM framework can be used as a more general function approximation technique. Lastly, we have combined our local methods with global approaches for planning to achieve arbitrary obstacle avoidance and considerable reduction in the computation complexity of the global planners.

EPFL2014

Chargement

Enactive robot vision

Mototaka Suzuki

EPFL2007

EPFL2014